Overview

Dataset statistics

Number of variables14
Number of observations5570
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory189.9 B

Variable types

Numeric13
Categorical1

Alerts

município has a high cardinality: 5570 distinct valuesHigh cardinality
cv_hepatite_b is highly overall correlated with cv_hib and 9 other fieldsHigh correlation
cv_hib is highly overall correlated with cv_hepatite_b and 9 other fieldsHigh correlation
cv_dpt is highly overall correlated with cv_hepatite_b and 9 other fieldsHigh correlation
cv_polio is highly overall correlated with cv_hepatite_b and 9 other fieldsHigh correlation
cv_rota is highly overall correlated with cv_hepatite_b and 9 other fieldsHigh correlation
cv_pneumo is highly overall correlated with cv_hepatite_b and 9 other fieldsHigh correlation
cv_mncc is highly overall correlated with cv_hepatite_b and 9 other fieldsHigh correlation
cv_scr1 is highly overall correlated with cv_hepatite_b and 8 other fieldsHigh correlation
cv_scr2 is highly overall correlated with cv_hepatite_b and 8 other fieldsHigh correlation
cv_varicela is highly overall correlated with cv_hepatite_b and 9 other fieldsHigh correlation
cv_hepatite_a is highly overall correlated with cv_hepatite_b and 9 other fieldsHigh correlation
município is uniformly distributedUniform
cod has unique valuesUnique
município has unique valuesUnique
cv_bcg has 431 (7.7%) zerosZeros
cv_scr2 has 136 (2.4%) zerosZeros

Reproduction

Analysis started2023-02-18 13:53:35.971669
Analysis finished2023-02-18 13:53:49.335176
Duration13.36 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

cod
Real number (ℝ)

Distinct5570
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean325358.63
Minimum110001
Maximum530010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-02-18T10:53:49.386602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum110001
5-th percentile150777.25
Q1251212.5
median314627.5
Q3411918.75
95-th percentile510729.55
Maximum530010
Range420009
Interquartile range (IQR)160706.25

Descriptive statistics

Standard deviation98491.034
Coefficient of variation (CV)0.3027153
Kurtosis-0.52580916
Mean325358.63
Median Absolute Deviation (MAD)74152.5
Skewness0.12134118
Sum1.8122476 × 109
Variance9.7004838 × 109
MonotonicityNot monotonic
2023-02-18T10:53:49.476335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110001 1
 
< 0.1%
353970 1
 
< 0.1%
354040 1
 
< 0.1%
354030 1
 
< 0.1%
354025 1
 
< 0.1%
354020 1
 
< 0.1%
354010 1
 
< 0.1%
354000 1
 
< 0.1%
353990 1
 
< 0.1%
353980 1
 
< 0.1%
Other values (5560) 5560
99.8%
ValueCountFrequency (%)
110001 1
< 0.1%
110002 1
< 0.1%
110003 1
< 0.1%
110004 1
< 0.1%
110005 1
< 0.1%
110006 1
< 0.1%
110007 1
< 0.1%
110008 1
< 0.1%
110009 1
< 0.1%
110010 1
< 0.1%
ValueCountFrequency (%)
530010 1
< 0.1%
522230 1
< 0.1%
522220 1
< 0.1%
522205 1
< 0.1%
522200 1
< 0.1%
522190 1
< 0.1%
522185 1
< 0.1%
522180 1
< 0.1%
522170 1
< 0.1%
522160 1
< 0.1%

município
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct5570
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size467.3 KiB
110001 Alta Floresta D'Oeste
 
1
353970 Platina
 
1
354040 Populina
 
1
354030 Pontes Gestal
 
1
354025 Pontalinda
 
1
Other values (5565)
5565 

Length

Max length39
Median length34
Mean length18.610592
Min length10

Characters and Unicode

Total characters103661
Distinct characters80
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5570 ?
Unique (%)100.0%

Sample

1st row110001 Alta Floresta D'Oeste
2nd row110002 Ariquemes
3rd row110003 Cabixi
4th row110004 Cacoal
5th row110005 Cerejeiras

Common Values

ValueCountFrequency (%)
110001 Alta Floresta D'Oeste 1
 
< 0.1%
353970 Platina 1
 
< 0.1%
354040 Populina 1
 
< 0.1%
354030 Pontes Gestal 1
 
< 0.1%
354025 Pontalinda 1
 
< 0.1%
354020 Pontal 1
 
< 0.1%
354010 Pongaí 1
 
< 0.1%
354000 Pompéia 1
 
< 0.1%
353990 Poloni 1
 
< 0.1%
353980 Poá 1
 
< 0.1%
Other values (5560) 5560
99.8%

Length

2023-02-18T10:53:49.566966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
do 756
 
4.8%
são 364
 
2.3%
de 302
 
1.9%
santa 161
 
1.0%
da 143
 
0.9%
nova 135
 
0.9%
sul 115
 
0.7%
rio 94
 
0.6%
dos 73
 
0.5%
josé 70
 
0.4%
Other values (9533) 13640
86.0%

Most occurring characters

ValueCountFrequency (%)
10283
 
9.9%
a 8791
 
8.5%
0 8160
 
7.9%
o 5961
 
5.8%
1 4774
 
4.6%
2 4591
 
4.4%
r 4532
 
4.4%
i 4388
 
4.2%
3 4106
 
4.0%
e 3764
 
3.6%
Other values (70) 44311
42.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50872
49.1%
Decimal Number 33420
32.2%
Space Separator 10283
 
9.9%
Uppercase Letter 9010
 
8.7%
Other Punctuation 47
 
< 0.1%
Dash Punctuation 29
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8791
17.3%
o 5961
11.7%
r 4532
8.9%
i 4388
8.6%
e 3764
 
7.4%
n 3196
 
6.3%
d 2553
 
5.0%
s 2423
 
4.8%
t 2293
 
4.5%
u 2155
 
4.2%
Other values (27) 10816
21.3%
Uppercase Letter
ValueCountFrequency (%)
S 1137
12.6%
C 970
10.8%
P 911
 
10.1%
M 721
 
8.0%
A 698
 
7.7%
B 602
 
6.7%
I 475
 
5.3%
J 405
 
4.5%
G 391
 
4.3%
R 367
 
4.1%
Other values (20) 2333
25.9%
Decimal Number
ValueCountFrequency (%)
0 8160
24.4%
1 4774
14.3%
2 4591
13.7%
3 4106
12.3%
5 3654
10.9%
4 2781
 
8.3%
7 1470
 
4.4%
6 1422
 
4.3%
9 1382
 
4.1%
8 1080
 
3.2%
Space Separator
ValueCountFrequency (%)
10283
100.0%
Other Punctuation
ValueCountFrequency (%)
' 47
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 59882
57.8%
Common 43779
42.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8791
14.7%
o 5961
 
10.0%
r 4532
 
7.6%
i 4388
 
7.3%
e 3764
 
6.3%
n 3196
 
5.3%
d 2553
 
4.3%
s 2423
 
4.0%
t 2293
 
3.8%
u 2155
 
3.6%
Other values (57) 19826
33.1%
Common
ValueCountFrequency (%)
10283
23.5%
0 8160
18.6%
1 4774
10.9%
2 4591
10.5%
3 4106
 
9.4%
5 3654
 
8.3%
4 2781
 
6.4%
7 1470
 
3.4%
6 1422
 
3.2%
9 1382
 
3.2%
Other values (3) 1156
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100822
97.3%
None 2839
 
2.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10283
 
10.2%
a 8791
 
8.7%
0 8160
 
8.1%
o 5961
 
5.9%
1 4774
 
4.7%
2 4591
 
4.6%
r 4532
 
4.5%
i 4388
 
4.4%
3 4106
 
4.1%
e 3764
 
3.7%
Other values (54) 41472
41.1%
None
ValueCountFrequency (%)
ã 794
28.0%
á 393
13.8%
í 336
11.8%
é 317
 
11.2%
ç 268
 
9.4%
ó 243
 
8.6%
â 161
 
5.7%
ú 101
 
3.6%
ô 71
 
2.5%
ê 70
 
2.5%
Other values (6) 85
 
3.0%

cv_bcg
Real number (ℝ)

Distinct3579
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.904652
Minimum0
Maximum594.72
Zeros431
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-02-18T10:53:49.659888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115.56
median48.72
Q380.6025
95-th percentile116.226
Maximum594.72
Range594.72
Interquartile range (IQR)65.0425

Descriptive statistics

Standard deviation42.85431
Coefficient of variation (CV)0.82563524
Kurtosis12.017241
Mean51.904652
Median Absolute Deviation (MAD)32.525
Skewness1.7814304
Sum289108.91
Variance1836.4919
MonotonicityNot monotonic
2023-02-18T10:53:49.748301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 431
 
7.7%
100 32
 
0.6%
50 23
 
0.4%
20 14
 
0.3%
75 13
 
0.2%
16.67 13
 
0.2%
66.67 12
 
0.2%
60 11
 
0.2%
6.67 11
 
0.2%
18.75 11
 
0.2%
Other values (3569) 4999
89.7%
ValueCountFrequency (%)
0 431
7.7%
0.11 1
 
< 0.1%
0.13 1
 
< 0.1%
0.21 1
 
< 0.1%
0.27 1
 
< 0.1%
0.31 1
 
< 0.1%
0.33 1
 
< 0.1%
0.39 2
 
< 0.1%
0.41 1
 
< 0.1%
0.47 1
 
< 0.1%
ValueCountFrequency (%)
594.72 1
< 0.1%
500.19 1
< 0.1%
473.92 1
< 0.1%
437.57 1
< 0.1%
396.37 1
< 0.1%
379.97 1
< 0.1%
340.54 1
< 0.1%
310 1
< 0.1%
291.8 1
< 0.1%
285.71 1
< 0.1%

cv_hepatite_b
Real number (ℝ)

Distinct3329
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.405007
Minimum0
Maximum430.77
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-02-18T10:53:49.844855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.3
Q170.43
median84.33
Q397.04
95-th percentile120
Maximum430.77
Range430.77
Interquartile range (IQR)26.61

Descriptive statistics

Standard deviation25.008494
Coefficient of variation (CV)0.29984404
Kurtosis10.122564
Mean83.405007
Median Absolute Deviation (MAD)13.23
Skewness0.72678474
Sum464565.89
Variance625.42478
MonotonicityNot monotonic
2023-02-18T10:53:49.931890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 90
 
1.6%
83.33 26
 
0.5%
66.67 20
 
0.4%
75 18
 
0.3%
80 18
 
0.3%
85.71 18
 
0.3%
90 18
 
0.3%
88.89 17
 
0.3%
71.43 16
 
0.3%
77.78 15
 
0.3%
Other values (3319) 5314
95.4%
ValueCountFrequency (%)
0 5
0.1%
0.26 1
 
< 0.1%
3.48 1
 
< 0.1%
3.51 1
 
< 0.1%
3.75 1
 
< 0.1%
4.76 1
 
< 0.1%
5.36 1
 
< 0.1%
6.12 1
 
< 0.1%
6.21 1
 
< 0.1%
6.67 2
 
< 0.1%
ValueCountFrequency (%)
430.77 1
< 0.1%
330 1
< 0.1%
250 1
< 0.1%
231.58 1
< 0.1%
222.57 1
< 0.1%
221.43 1
< 0.1%
216 1
< 0.1%
200 1
< 0.1%
197.73 1
< 0.1%
190.91 1
< 0.1%

cv_hib
Real number (ℝ)

Distinct3322
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.427639
Minimum0
Maximum423.08
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-02-18T10:53:50.022704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.3045
Q170.4725
median84.38
Q397.1075
95-th percentile120
Maximum423.08
Range423.08
Interquartile range (IQR)26.635

Descriptive statistics

Standard deviation25.006877
Coefficient of variation (CV)0.29974332
Kurtosis9.5513955
Mean83.427639
Median Absolute Deviation (MAD)13.265
Skewness0.69381693
Sum464691.95
Variance625.34391
MonotonicityNot monotonic
2023-02-18T10:53:50.110274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 93
 
1.7%
83.33 24
 
0.4%
88.89 18
 
0.3%
90 18
 
0.3%
80 18
 
0.3%
75 17
 
0.3%
66.67 17
 
0.3%
85.71 16
 
0.3%
71.43 15
 
0.3%
87.5 14
 
0.3%
Other values (3312) 5320
95.5%
ValueCountFrequency (%)
0 5
0.1%
0.26 1
 
< 0.1%
3.48 1
 
< 0.1%
3.51 1
 
< 0.1%
3.75 1
 
< 0.1%
4.76 1
 
< 0.1%
5.36 1
 
< 0.1%
5.92 1
 
< 0.1%
6.12 1
 
< 0.1%
6.67 2
 
< 0.1%
ValueCountFrequency (%)
423.08 1
< 0.1%
330 1
< 0.1%
250 1
< 0.1%
231.58 1
< 0.1%
221.63 1
< 0.1%
221.43 1
< 0.1%
216 1
< 0.1%
200 1
< 0.1%
197.73 1
< 0.1%
190.91 1
< 0.1%

cv_dpt
Real number (ℝ)

Distinct3305
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.545379
Minimum0
Maximum423.08
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-02-18T10:53:50.202716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.419
Q170.59
median84.48
Q397.2275
95-th percentile120
Maximum423.08
Range423.08
Interquartile range (IQR)26.6375

Descriptive statistics

Standard deviation24.999024
Coefficient of variation (CV)0.29922689
Kurtosis9.5637004
Mean83.545379
Median Absolute Deviation (MAD)13.19
Skewness0.69679203
Sum465347.76
Variance624.95118
MonotonicityNot monotonic
2023-02-18T10:53:50.290271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 93
 
1.7%
83.33 27
 
0.5%
80 22
 
0.4%
90 18
 
0.3%
88.89 18
 
0.3%
75 17
 
0.3%
71.43 15
 
0.3%
85.71 14
 
0.3%
66.67 14
 
0.3%
87.5 13
 
0.2%
Other values (3295) 5319
95.5%
ValueCountFrequency (%)
0 5
0.1%
0.26 1
 
< 0.1%
3.48 1
 
< 0.1%
3.51 1
 
< 0.1%
3.75 1
 
< 0.1%
4.76 1
 
< 0.1%
5.36 1
 
< 0.1%
5.92 1
 
< 0.1%
6.12 1
 
< 0.1%
6.67 2
 
< 0.1%
ValueCountFrequency (%)
423.08 1
< 0.1%
330 1
< 0.1%
250 1
< 0.1%
231.58 1
< 0.1%
221.63 1
< 0.1%
221.43 1
< 0.1%
216 1
< 0.1%
200 1
< 0.1%
197.73 1
< 0.1%
190.91 1
< 0.1%

cv_polio
Real number (ℝ)

Distinct3312
Distinct (%)59.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.487752
Minimum0
Maximum384.62
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-02-18T10:53:50.382030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.366
Q169.27
median83.465
Q395.9975
95-th percentile118.4525
Maximum384.62
Range384.62
Interquartile range (IQR)26.7275

Descriptive statistics

Standard deviation24.683083
Coefficient of variation (CV)0.29923331
Kurtosis7.8084147
Mean82.487752
Median Absolute Deviation (MAD)13.18
Skewness0.59243741
Sum459456.78
Variance609.25458
MonotonicityNot monotonic
2023-02-18T10:53:50.550416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 89
 
1.6%
80 22
 
0.4%
75 21
 
0.4%
66.67 19
 
0.3%
83.33 18
 
0.3%
85.71 18
 
0.3%
88.89 18
 
0.3%
90 16
 
0.3%
50 14
 
0.3%
91.67 14
 
0.3%
Other values (3302) 5321
95.5%
ValueCountFrequency (%)
0 5
0.1%
0.26 1
 
< 0.1%
1.62 1
 
< 0.1%
1.75 1
 
< 0.1%
3.48 1
 
< 0.1%
3.75 1
 
< 0.1%
4.08 1
 
< 0.1%
5.92 1
 
< 0.1%
5.95 1
 
< 0.1%
6.67 3
0.1%
ValueCountFrequency (%)
384.62 1
< 0.1%
330 1
< 0.1%
250 1
< 0.1%
234.21 1
< 0.1%
228.57 1
< 0.1%
224 1
< 0.1%
219.44 1
< 0.1%
200 1
< 0.1%
197.73 1
< 0.1%
190.91 1
< 0.1%

cv_rota
Real number (ℝ)

Distinct3355
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.201244
Minimum0
Maximum370
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-02-18T10:53:50.648060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39.573
Q168.54
median83.235
Q396
95-th percentile120
Maximum370
Range370
Interquartile range (IQR)27.46

Descriptive statistics

Standard deviation25.178006
Coefficient of variation (CV)0.30629713
Kurtosis7.5732395
Mean82.201244
Median Absolute Deviation (MAD)13.735
Skewness0.59947349
Sum457860.93
Variance633.93196
MonotonicityNot monotonic
2023-02-18T10:53:50.736429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 84
 
1.5%
75 24
 
0.4%
66.67 17
 
0.3%
50 17
 
0.3%
87.5 16
 
0.3%
83.33 16
 
0.3%
80 16
 
0.3%
120 15
 
0.3%
71.43 15
 
0.3%
88.89 14
 
0.3%
Other values (3345) 5336
95.8%
ValueCountFrequency (%)
0 8
0.1%
0.72 1
 
< 0.1%
0.87 1
 
< 0.1%
1.75 1
 
< 0.1%
3.85 1
 
< 0.1%
4 1
 
< 0.1%
5.26 1
 
< 0.1%
5.56 1
 
< 0.1%
5.77 1
 
< 0.1%
5.95 1
 
< 0.1%
ValueCountFrequency (%)
370 1
< 0.1%
353.85 1
< 0.1%
271.43 1
< 0.1%
268 1
< 0.1%
228.57 1
< 0.1%
207.69 1
< 0.1%
200 2
< 0.1%
192.54 1
< 0.1%
185.71 1
< 0.1%
181.82 1
< 0.1%

cv_pneumo
Real number (ℝ)

Distinct3363
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.166591
Minimum0
Maximum440
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-02-18T10:53:50.833309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.149
Q171.865
median86.11
Q398.78
95-th percentile123.08
Maximum440
Range440
Interquartile range (IQR)26.915

Descriptive statistics

Standard deviation25.364453
Coefficient of variation (CV)0.29782164
Kurtosis11.559409
Mean85.166591
Median Absolute Deviation (MAD)13.45
Skewness0.82006803
Sum474377.91
Variance643.35549
MonotonicityNot monotonic
2023-02-18T10:53:50.920250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 82
 
1.5%
83.33 23
 
0.4%
75 23
 
0.4%
66.67 21
 
0.4%
80 15
 
0.3%
90 15
 
0.3%
90.48 15
 
0.3%
88.89 14
 
0.3%
120 13
 
0.2%
90.91 12
 
0.2%
Other values (3353) 5337
95.8%
ValueCountFrequency (%)
0 7
0.1%
1.08 1
 
< 0.1%
1.74 1
 
< 0.1%
2.67 1
 
< 0.1%
3.51 1
 
< 0.1%
3.75 1
 
< 0.1%
5.95 1
 
< 0.1%
6.67 2
 
< 0.1%
6.94 1
 
< 0.1%
7.14 1
 
< 0.1%
ValueCountFrequency (%)
440 1
< 0.1%
369.23 1
< 0.1%
260 1
< 0.1%
257.14 1
< 0.1%
228.57 1
< 0.1%
208.33 1
< 0.1%
207.69 1
< 0.1%
205.97 1
< 0.1%
200 2
< 0.1%
190.91 1
< 0.1%

cv_mncc
Real number (ℝ)

Distinct3343
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.748248
Minimum0
Maximum400
Zeros9
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-02-18T10:53:51.010934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.427
Q170
median83.61
Q396.24
95-th percentile118.9915
Maximum400
Range400
Interquartile range (IQR)26.24

Descriptive statistics

Standard deviation24.672326
Coefficient of variation (CV)0.29816131
Kurtosis10.248185
Mean82.748248
Median Absolute Deviation (MAD)13.085
Skewness0.69826304
Sum460907.74
Variance608.72365
MonotonicityNot monotonic
2023-02-18T10:53:51.103580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 97
 
1.7%
80 25
 
0.4%
75 20
 
0.4%
88.89 20
 
0.4%
66.67 20
 
0.4%
83.33 19
 
0.3%
50 14
 
0.3%
81.25 14
 
0.3%
71.43 14
 
0.3%
81.82 13
 
0.2%
Other values (3333) 5314
95.4%
ValueCountFrequency (%)
0 9
0.2%
2.67 1
 
< 0.1%
2.86 1
 
< 0.1%
3.51 1
 
< 0.1%
3.75 1
 
< 0.1%
5 1
 
< 0.1%
5.41 1
 
< 0.1%
6.67 1
 
< 0.1%
7.14 2
 
< 0.1%
7.41 1
 
< 0.1%
ValueCountFrequency (%)
400 1
< 0.1%
376.92 1
< 0.1%
272 1
< 0.1%
218.18 1
< 0.1%
214.29 1
< 0.1%
207.69 1
< 0.1%
200 1
< 0.1%
181.25 1
< 0.1%
179.1 1
< 0.1%
177.14 1
< 0.1%

cv_scr1
Real number (ℝ)

Distinct3402
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.066312
Minimum0
Maximum430
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-02-18T10:53:51.203109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.8545
Q173.2175
median88.995
Q3102.5
95-th percentile128.891
Maximum430
Range430
Interquartile range (IQR)29.2825

Descriptive statistics

Standard deviation27.731964
Coefficient of variation (CV)0.31489866
Kurtosis8.8601778
Mean88.066312
Median Absolute Deviation (MAD)14.455
Skewness0.87639996
Sum490529.36
Variance769.06182
MonotonicityNot monotonic
2023-02-18T10:53:51.289462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 119
 
2.1%
75 21
 
0.4%
85.71 18
 
0.3%
80 17
 
0.3%
83.33 16
 
0.3%
71.43 14
 
0.3%
116.67 14
 
0.3%
133.33 12
 
0.2%
125 11
 
0.2%
110 11
 
0.2%
Other values (3392) 5317
95.5%
ValueCountFrequency (%)
0 8
0.1%
2.5 1
 
< 0.1%
3.11 1
 
< 0.1%
3.57 1
 
< 0.1%
4 2
 
< 0.1%
4.05 1
 
< 0.1%
4.48 1
 
< 0.1%
6.02 1
 
< 0.1%
6.14 1
 
< 0.1%
6.25 1
 
< 0.1%
ValueCountFrequency (%)
430 1
< 0.1%
369.23 1
< 0.1%
288.46 1
< 0.1%
285.71 1
< 0.1%
252.17 1
< 0.1%
252 1
< 0.1%
250 1
< 0.1%
232.76 1
< 0.1%
229.63 1
< 0.1%
228.57 1
< 0.1%

cv_scr2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3642
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.942772
Minimum0
Maximum410
Zeros136
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-02-18T10:53:51.379520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.67
Q137.3525
median60.98
Q382.25
95-th percentile109.09
Maximum410
Range410
Interquartile range (IQR)44.8975

Descriptive statistics

Standard deviation31.762169
Coefficient of variation (CV)0.52987487
Kurtosis2.8876502
Mean59.942772
Median Absolute Deviation (MAD)22.35
Skewness0.40970756
Sum333881.24
Variance1008.8354
MonotonicityNot monotonic
2023-02-18T10:53:51.466259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 136
 
2.4%
100 59
 
1.1%
66.67 24
 
0.4%
50 21
 
0.4%
83.33 18
 
0.3%
75 18
 
0.3%
33.33 15
 
0.3%
84.62 14
 
0.3%
25 14
 
0.3%
37.5 13
 
0.2%
Other values (3632) 5238
94.0%
ValueCountFrequency (%)
0 136
2.4%
0.21 1
 
< 0.1%
0.26 1
 
< 0.1%
0.29 1
 
< 0.1%
0.43 1
 
< 0.1%
0.53 1
 
< 0.1%
0.58 1
 
< 0.1%
0.61 1
 
< 0.1%
0.7 1
 
< 0.1%
0.71 1
 
< 0.1%
ValueCountFrequency (%)
410 1
< 0.1%
292.31 1
< 0.1%
233.33 1
< 0.1%
228 1
< 0.1%
200 1
< 0.1%
182.61 1
< 0.1%
180 1
< 0.1%
161.54 1
< 0.1%
158.82 1
< 0.1%
157.14 1
< 0.1%

cv_varicela
Real number (ℝ)

Distinct3545
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.095734
Minimum0
Maximum370
Zeros14
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-02-18T10:53:51.555439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.707
Q161.915
median81.25
Q396.6775
95-th percentile125
Maximum370
Range370
Interquartile range (IQR)34.7625

Descriptive statistics

Standard deviation30.179107
Coefficient of variation (CV)0.37678795
Kurtosis3.8859984
Mean80.095734
Median Absolute Deviation (MAD)17
Skewness0.62460514
Sum446133.24
Variance910.77853
MonotonicityNot monotonic
2023-02-18T10:53:51.646346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 66
 
1.2%
75 21
 
0.4%
80 20
 
0.4%
66.67 17
 
0.3%
87.5 16
 
0.3%
83.33 15
 
0.3%
120 15
 
0.3%
0 14
 
0.3%
88.89 14
 
0.3%
50 13
 
0.2%
Other values (3535) 5359
96.2%
ValueCountFrequency (%)
0 14
0.3%
0.3 1
 
< 0.1%
0.91 1
 
< 0.1%
1.3 1
 
< 0.1%
2.04 1
 
< 0.1%
2.42 1
 
< 0.1%
2.78 1
 
< 0.1%
2.86 1
 
< 0.1%
3.51 1
 
< 0.1%
3.88 1
 
< 0.1%
ValueCountFrequency (%)
370 1
< 0.1%
284.62 1
< 0.1%
264.29 1
< 0.1%
254.55 1
< 0.1%
242.42 1
< 0.1%
233.33 1
< 0.1%
231.58 1
< 0.1%
228.26 1
< 0.1%
226.47 1
< 0.1%
222.89 1
< 0.1%

cv_hepatite_a
Real number (ℝ)

Distinct3370
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.592856
Minimum0
Maximum470
Zeros24
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-02-18T10:53:51.742239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31.3725
Q164.715
median81.48
Q395.6875
95-th percentile119.05
Maximum470
Range470
Interquartile range (IQR)30.9725

Descriptive statistics

Standard deviation27.246348
Coefficient of variation (CV)0.34232152
Kurtosis11.153753
Mean79.592856
Median Absolute Deviation (MAD)15.28
Skewness0.64918029
Sum443332.21
Variance742.36348
MonotonicityNot monotonic
2023-02-18T10:53:51.830107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 97
 
1.7%
75 29
 
0.5%
50 24
 
0.4%
0 24
 
0.4%
66.67 21
 
0.4%
85.71 19
 
0.3%
80 19
 
0.3%
83.33 16
 
0.3%
81.82 13
 
0.2%
111.11 13
 
0.2%
Other values (3360) 5295
95.1%
ValueCountFrequency (%)
0 24
0.4%
0.25 1
 
< 0.1%
0.36 1
 
< 0.1%
1.11 1
 
< 0.1%
1.24 1
 
< 0.1%
1.61 1
 
< 0.1%
1.8 1
 
< 0.1%
1.85 1
 
< 0.1%
1.92 1
 
< 0.1%
2.38 1
 
< 0.1%
ValueCountFrequency (%)
470 1
< 0.1%
376.92 1
< 0.1%
271.43 1
< 0.1%
240 1
< 0.1%
211.11 1
< 0.1%
200 1
< 0.1%
192.86 1
< 0.1%
190 1
< 0.1%
188.89 1
< 0.1%
183.33 1
< 0.1%

Interactions

2023-02-18T10:53:48.084240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:36.690280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:37.656885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:38.597846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:39.506040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:40.472003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:41.366227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:42.343285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:43.373402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:44.274966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:45.253708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:46.222524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:47.119583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:48.150697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:36.760673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:37.725701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:38.665191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:39.573038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:40.537703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:41.438115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:42.413968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:43.440651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:44.347919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:45.320189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:46.288327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:47.190543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:48.222433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:36.882143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:37.800242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:38.736324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:39.643974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:40.608782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:41.516123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:42.489886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:43.511770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:44.425052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:45.390928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:46.359593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:47.267295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:48.288903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:36.948992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:37.868701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:38.803010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:39.710481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:40.673963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:41.587415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:42.559765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:43.576857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:44.497732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:45.457705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:46.424884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:47.337466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:48.355418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:37.015884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:37.937336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:38.869468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:39.775295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:40.739052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:41.659692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:42.630024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:43.643048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:44.568819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:45.522934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:46.490315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:47.408489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:48.420833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:37.082350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:38.006352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:38.934987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:39.840862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:40.803842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:41.730885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:42.700188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:43.707978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:44.641115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:45.588068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:46.555197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:47.481347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:48.498340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:37.160968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:38.087496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:39.013028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:39.986099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:40.879691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:41.814426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:42.851744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:43.785563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:44.723347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:45.737271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:46.633803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:47.563532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:48.573591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:37.236776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:38.164649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:39.087626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:40.060027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:40.954213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:41.894621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:42.929752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:43.859378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:44.804365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:45.811274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:46.707658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:47.642388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:48.639570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:37.302954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:38.234232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:39.154275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:40.125196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:41.018956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:41.965849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:43.000719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:43.925848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:44.876354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:45.876157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:46.773478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:47.712572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:48.791060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:37.381760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:38.314182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:39.231994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:40.202837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:41.096385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:42.048726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:43.082178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:44.002556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:44.958475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:45.953050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:46.849849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:47.794479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:48.856889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:37.447771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:38.383376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:39.299062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:40.267743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:41.160872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:42.119981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:43.151472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:44.068841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:45.030323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:46.017856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:46.914224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:47.864417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:48.922154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:37.514491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:38.451491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:39.365104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:40.332921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:41.226489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:42.191982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:43.222774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:44.134338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:45.101462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:46.083238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:46.979703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:47.935130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:48.997129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:37.589295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:38.529493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:39.440433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:40.406595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:41.300072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:42.271559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:43.303097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:44.209746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:45.182743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:46.157099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:47.053535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T10:53:48.013405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-18T10:53:51.983736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
codcv_bcgcv_hepatite_bcv_hibcv_dptcv_poliocv_rotacv_pneumocv_mncccv_scr1cv_scr2cv_varicelacv_hepatite_a
cod1.0000.2490.1530.1540.1540.1680.1910.1560.1680.1950.1660.1630.226
cv_bcg0.2491.0000.2490.2500.2500.2570.2940.2950.2780.2250.2590.1780.256
cv_hepatite_b0.1530.2491.0001.0001.0000.9800.8860.8840.9080.7410.5360.6920.739
cv_hib0.1540.2501.0001.0001.0000.9800.8870.8850.9090.7420.5370.6930.740
cv_dpt0.1540.2501.0001.0001.0000.9800.8870.8850.9090.7420.5370.6920.740
cv_polio0.1680.2570.9800.9800.9801.0000.8940.8910.9160.7410.5410.6980.748
cv_rota0.1910.2940.8860.8870.8870.8941.0000.9750.9250.7240.5430.6760.733
cv_pneumo0.1560.2950.8840.8850.8850.8910.9751.0000.9290.7110.5150.6490.712
cv_mncc0.1680.2780.9080.9090.9090.9160.9250.9291.0000.7310.5230.6740.732
cv_scr10.1950.2250.7410.7420.7420.7410.7240.7110.7311.0000.4990.7560.813
cv_scr20.1660.2590.5360.5370.5370.5410.5430.5150.5230.4991.0000.6510.655
cv_varicela0.1630.1780.6920.6930.6920.6980.6760.6490.6740.7560.6511.0000.859
cv_hepatite_a0.2260.2560.7390.7400.7400.7480.7330.7120.7320.8130.6550.8591.000

Missing values

2023-02-18T10:53:49.100781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-18T10:53:49.263931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

codmunicípiocv_bcgcv_hepatite_bcv_hibcv_dptcv_poliocv_rotacv_pneumocv_mncccv_scr1cv_scr2cv_varicelacv_hepatite_a
0110001110001 Alta Floresta D'Oeste67.5793.6993.6993.9991.2993.6997.0097.00115.1161.6388.2291.24
1110002110002 Ariquemes108.6983.2183.2883.2883.6885.0089.0588.3287.1759.4978.8880.82
2110003110003 Cabixi0.0092.7592.7592.7594.20101.45101.4589.8698.5194.0397.0197.01
3110004110004 Cacoal108.9888.4589.2889.2889.1390.0491.8592.75135.8427.3282.1982.04
4110005110005 Cerejeiras63.9492.1992.9492.9494.0595.5498.1495.91103.3783.9093.6392.88
5110006110006 Colorado do Oeste19.2189.6689.6689.6689.1693.6097.0493.10100.0084.7399.51102.46
6110007110007 Corumbiara14.0280.3780.3780.3786.9292.5295.3391.59106.86120.59124.51124.51
7110008110008 Costa Marques77.3795.2695.2695.7992.11102.63106.3299.47109.1983.7892.4392.97
8110009110009 Espigão D'Oeste91.3083.4483.4483.4484.0894.2797.6695.3392.292.7890.3686.94
9110010110010 Guajará-Mirim62.1247.3247.3247.3246.3946.2747.7946.5052.4415.9737.5438.50
codmunicípiocv_bcgcv_hepatite_bcv_hibcv_dptcv_poliocv_rotacv_pneumocv_mncccv_scr1cv_scr2cv_varicelacv_hepatite_a
5560522160522160 Uruaçu93.5886.2386.2386.2380.9489.6293.7788.3093.7140.7665.3381.33
5561522170522170 Uruana22.9672.5972.5972.5971.8577.7886.6779.2676.8748.5163.4368.66
5562522180522180 Urutaí95.83170.83170.83170.83166.67150.00145.83175.00170.8320.83125.00129.17
5563522185522185 Valparaíso de Goiás20.6279.9479.9480.1080.7579.5882.0879.6677.8435.9051.0274.00
5564522190522190 Varjão37.93131.03131.03131.03131.03124.14127.59117.24111.11122.22203.70155.56
5565522200522200 Vianópolis125.00104.65104.65105.2399.42104.07111.63101.16105.8587.13119.88115.20
5566522205522205 Vicentinópolis9.3556.1256.1256.1257.5553.9654.6847.4872.4613.7734.0626.81
5567522220522220 Vila Boa64.71131.37131.37131.37129.41127.45131.37133.33123.5374.5182.35125.49
5568522230522230 Vila Propício28.3067.9267.9267.9267.9260.3867.9262.2656.6033.9643.4052.83
5569530010530010 Brasília104.8578.8278.7778.7678.7480.9084.4181.7587.1257.4678.9081.75